Take Home Exercise 3

Uncover illegal, unreported, and unregulated (IUU) fishing activities through visual analytics

Task

Data Wraggling

Load Packages

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pacman::p_load(jsonlite, tidygraph, ggraph, visNetwork, graphlayouts, ggforce,skimr,tidytext, tidyverse)

Data Import

In the code chunk below, fromJSON() of jsonlite package is used to import MC3.json into R environment.

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mc3_data <- fromJSON("data/MC3.json")

Examine the data, this is not a directed graph, not looking into in- and out-degree of the nodes.

Extracting edges

Below code chunk changes the links field into character field.

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mc3_edges <- as_tibble(mc3_data$links)%>%
  distinct() %>%
  mutate(source = as.character(source),
         target = as.character(target),
         type = as.character(type)) %>%
  group_by(source, target, type) %>%
    summarise(weights = n()) %>%
  filter(source!=target)%>%
  ungroup

Extracting nodes

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mc3_nodes <- as_tibble(mc3_data$nodes) %>%
#  distinct()%>%
  mutate(country = as.character(country),
         id = as.character(id),
         product_services = as.character(product_services),
         revenue_omu = as.numeric(as.character(revenue_omu)),
         type = as.character(type)) %>%
    select(id, country, type, revenue_omu, product_services)

Initial Data Exploration

Exploring the edges data frame

In the code chunk below, skim() of skimr package is used to display the summary statistics of mc3_edges tibble data frame.

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skim(mc3_edges)

Data summary

Name	mc3_edges
Number of rows	24036
Number of columns	4
_______________________
Column type frequency:
character	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	6	700	0	12856	0
target	0	1	6	28	0	21265	0
type	0	1	16	16	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
weights	0	1	1	0	1	1	1	1	1	▁▁▇▁▁

The report above reveals that there is not missing values in all fields.

In the code chunk below, datatable() of DT package is used to display mc3_edges tibble data frame as an interactive table on the html document.

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DT::datatable(mc3_edges)

Convert character(0) for type to NA for text sensing

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ggplot(data = mc3_edges,
       aes(x=type)) +
  geom_bar()

Initial Network Visualization and Analysis

Building network model with tidygraph

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id1 <- mc3_edges %>%
  select(source) %>%
  rename(id = source)
id2 <- mc3_edges %>%
  select(target) %>%
  rename(id = target)
mc3_nodes1 <- rbind(id1, id2) %>%
  distinct() %>%
  left_join(mc3_nodes,
            unmatched = "drop")

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mc3_graph <- tbl_graph(nodes = mc3_nodes1,
                       edges = mc3_edges,
                       directed = FALSE) %>%
  mutate(betweenness_centrality = centrality_betweenness(),
         closeness_centrality = centrality_closeness())

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mc3_graph %>%
  filter(betweenness_centrality >= 100000) %>%
ggraph(layout = "fr") +
  geom_edge_link(aes(alpha=0.5)) +
  geom_node_point(aes(
    size = betweenness_centrality,
    colors = "lightblue",
    alpha = 0.5)) +
  scale_size_continuous(range=c(1,10))+
  theme_graph()

Exploring the nodes dataframe

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skim(mc3_nodes)

Data summary

Name	mc3_nodes
Number of rows	27622
Number of columns	5
_______________________
Column type frequency:
character	4
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
id	0	1	6	64	0	22929	0
country	0	1	2	15	0	100	0
type	0	1	7	16	0	3	0
product_services	0	1	4	1737	0	3244	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
revenue_omu	21515	0.22	1822155	18184433	3652.23	7676.36	16210.68	48327.66	310612303	▇▁▁▁▁

The report above reveals that there is no missing values in all fields.

In the code chunk below, datatable() of DT package is used to display mc3_nodes tibble data frame as an interactive table on the html document.

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DT::datatable(mc3_nodes)

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ggplot(data = mc3_nodes,
       aes(x = type)) +
  geom_bar()

Text Sensing with tidytext

Simple word count

The code chunk below calculates number of times the word fish appeared in the field product_services.

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mc3_nodes %>% 
    mutate(n_fish = str_count(product_services, "fish")) 

# A tibble: 27,622 × 6
   id                          country type  revenue_omu product_services n_fish
   <chr>                       <chr>   <chr>       <dbl> <chr>             <int>
 1 Jones LLC                   ZH      Comp…  310612303. Automobiles           0
 2 Coleman, Hall and Lopez     ZH      Comp…  162734684. Passenger cars,…      0
 3 Aqua Advancements Sashimi … Oceanus Comp…  115004667. Holding firm wh…      0
 4 Makumba Ltd. Liability Co   Utopor… Comp…   90986413. Car service, ca…      0
 5 Taylor, Taylor and Farrell  ZH      Comp…   81466667. Fully electric …      0
 6 Harmon, Edwards and Bates   ZH      Comp…   75070435. Discount superm…      0
 7 Punjab s Marine conservati… Riodel… Comp…   72167572. Beef, pork, chi…      0
 8 Assam   Limited Liability … Utopor… Comp…   72162317. Power and Gas s…      0
 9 Ianira Starfish Sagl Import Rio Is… Comp…   68832979. Light commercia…      0
10 Moran, Lewis and Jimenez    ZH      Comp…   65592906. Automobiles, tr…      0
# ℹ 27,612 more rows

Tokenisation

The word tokenisation have different meaning in different scientific domains. In text sensing, tokenisation is the process of breaking up a given text into units called tokens. Tokens can be individual words, phrases or even whole sentences. In the process of tokenisation, some characters like punctuation marks may be discarded. The tokens usually become the input for the processes like parsing and text mining.

In the code chunk below, unnest_token() of tidytext is used to split text in product_services field into words.

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token_nodes <- mc3_nodes %>%
  unnest_tokens(word, 
                product_services)

The two basic arguments to unnest_tokens() used here are column names. First we have the output column name that will be created as the text is unnested into it (word, in this case), and then the input column that the text comes from (product_services, in this case).

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token_nodes %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
      labs(x = "Count",
      y = "Unique words",
      title = "Count of unique words found in product_services field")

The bar chart reveals that the unique words contains some words that may not be useful to use. For instance "a" and "to". In the word of text mining we call those words stop words. You want to remove these words from your analysis as they are fillers used to compose a sentence.

Removing stopwords

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stopwords_removed <- token_nodes %>% 
  anti_join(stop_words)

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stopwords_removed %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
      labs(x = "Count",
      y = "Unique words",
      title = "Count of unique words found in product_services field")